IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v149y2020icp951-961.html
   My bibliography  Save this article

Artificial neural network modeling of performance, emission, and vibration of a CI engine using alumina nano-catalyst added to diesel-biodiesel blends

Author

Listed:
  • Hosseini, Seyyed Hassan
  • Taghizadeh-Alisaraei, Ahmad
  • Ghobadian, Barat
  • Abbaszadeh-Mayvan, Ahmad

Abstract

In recent years, added nano-catalysts to fuels has improved their thermo-physical properties. In present study, the alumina as additive with concentrations of 30, 60, and 90 ppm were added to B5 and B10 blends for evaluation of the engine performance, emissions, and vibration levels. An ANN model based on standard back-propagation learning algorithm for the engine was developed. Multi-layer perception network (MLP) was used for a non-linear mapping between the input and target parameters. The input or independent parameters were fuel blend, engine speed, fuel density, fuel viscosity, LHV, intake manifold pressure, fuel consumption, exhaust gas temperature, oxygen contained in exhaust gases, oil temperature, relative humidity, and ambient air pressure. Whereas, the target parameters separately were engine power, torque, emissions of CO, CO2, UHC, NO, RMS and Kurtosis of engine’s vibration. The results for optimum ANN model showed, the training algorithm of back-propagation with 25-25 neurons in hidden layers (logsig-logsig) is able to predict different parameters of engine for different conditions. The corresponding R-values for training, validation and testing were 0.9999, 0.9994 and 0.9995, respectively. The performance and accuracy of the proposed ANN model was completely satisfactory.

Suggested Citation

  • Hosseini, Seyyed Hassan & Taghizadeh-Alisaraei, Ahmad & Ghobadian, Barat & Abbaszadeh-Mayvan, Ahmad, 2020. "Artificial neural network modeling of performance, emission, and vibration of a CI engine using alumina nano-catalyst added to diesel-biodiesel blends," Renewable Energy, Elsevier, vol. 149(C), pages 951-961.
    • Handle: RePEc:eee:renene:v:149:y:2020:i:c:p:951-961
    DOI: 10.1016/j.renene.2019.10.080
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S096014811931571X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2019.10.080?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hosseini, Seyyed Hassan & Taghizadeh-Alisaraei, Ahmad & Ghobadian, Barat & Abbaszadeh-Mayvan, Ahmad, 2017. "Effect of added alumina as nano-catalyst to diesel-biodiesel blends on performance and emission characteristics of CI engine," Energy, Elsevier, vol. 124(C), pages 543-552.
    2. Hosseini, Seyyed Hassan & Taghizadeh-Alisaraei, Ahmad & Ghobadian, Barat & Abbaszadeh-Mayvan, Ahmad, 2017. "Performance and emission characteristics of a CI engine fuelled with carbon nanotubes and diesel-biodiesel blends," Renewable Energy, Elsevier, vol. 111(C), pages 201-213.
    3. Ghobadian, B. & Rahimi, H. & Nikbakht, A.M. & Najafi, G. & Yusaf, T.F., 2009. "Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network," Renewable Energy, Elsevier, vol. 34(4), pages 976-982.
    4. Najafi, Gholamhassan & Ghobadian, Barat & Yusaf, Talal F., 2011. "Algae as a sustainable energy source for biofuel production in Iran: A case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(8), pages 3870-3876.
    5. Canakci, Mustafa & Erdil, Ahmet & Arcaklioglu, Erol, 2006. "Performance and exhaust emissions of a biodiesel engine," Applied Energy, Elsevier, vol. 83(6), pages 594-605, June.
    6. Mohamed Ismail, Harun & Ng, Hoon Kiat & Queck, Cheen Wei & Gan, Suyin, 2012. "Artificial neural networks modelling of engine-out responses for a light-duty diesel engine fuelled with biodiesel blends," Applied Energy, Elsevier, vol. 92(C), pages 769-777.
    7. Tan, Pi-qiang & Ruan, Shuai-shuai & Hu, Zhi-yuan & Lou, Di-ming & Li, Hu, 2014. "Particle number emissions from a light-duty diesel engine with biodiesel fuels under transient-state operating conditions," Applied Energy, Elsevier, vol. 113(C), pages 22-31.
    8. Yusaf, Talal F. & Buttsworth, D.R. & Saleh, Khalid H. & Yousif, B.F., 2010. "CNG-diesel engine performance and exhaust emission analysis with the aid of artificial neural network," Applied Energy, Elsevier, vol. 87(5), pages 1661-1669, May.
    9. Shaafi, T. & Sairam, K. & Gopinath, A. & Kumaresan, G. & Velraj, R., 2015. "Effect of dispersion of various nanoadditives on the performance and emission characteristics of a CI engine fuelled with diesel, biodiesel and blends—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 563-573.
    10. Rezaei, Javad & Shahbakhti, Mahdi & Bahri, Bahram & Aziz, Azhar Abdul, 2015. "Performance prediction of HCCI engines with oxygenated fuels using artificial neural networks," Applied Energy, Elsevier, vol. 138(C), pages 460-473.
    11. Deh Kiani, M. Kiani & Ghobadian, B. & Tavakoli, T. & Nikbakht, A.M. & Najafi, G., 2010. "Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol- gasoline blends," Energy, Elsevier, vol. 35(1), pages 65-69.
    12. Selim, Mohamed Y.E, 2001. "Pressure–time characteristics in diesel engine fueled with natural gas," Renewable Energy, Elsevier, vol. 22(4), pages 473-489.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Li, Ji & Wu, Dawei & Mohammadsami Attar, Hassan & Xu, Hongming, 2022. "Geometric neuro-fuzzy transfer learning for in-cylinder pressure modelling of a diesel engine fuelled with raw microalgae oil," Applied Energy, Elsevier, vol. 306(PA).
    2. Krochmalny, Krystian & Niedzwiecki, Lukasz & Pelińska-Olko, Ewa & Wnukowski, Mateusz & Czajka, Krzysztof & Tkaczuk-Serafin, Monika & Pawlak-Kruczek, Halina, 2020. "Determination of the marker for automation of torrefaction and slow pyrolysis processes – A case study of spherical wood particles," Renewable Energy, Elsevier, vol. 161(C), pages 350-360.
    3. Zandie, Mohammad & Ng, Hoon Kiat & Gan, Suyin & Muhamad Said, Mohd Farid & Cheng, Xinwei, 2023. "Multi-input multi-output machine learning predictive model for engine performance and stability, emissions, combustion and ignition characteristics of diesel-biodiesel-gasoline blends," Energy, Elsevier, vol. 262(PA).
    4. Chen, Yong & Lu, Zhiyuan & Liu, Heng & Wang, Hu & Zheng, Zunqing & Wang, Changhui & Sun, Xingyu & Xu, Linxun & Yao, Mingfa, 2024. "Machine learning-based design of target property-oriented fuels using explainable artificial intelligence," Energy, Elsevier, vol. 300(C).
    5. N, Santhosh & Afzal, Asif & V, Srikanth H. & Ağbulut, Ümit & Alahmadi, Ahmad Aziz & Gowda, Ashwin C. & Alwetaishi, Mamdooh & Shaik, Saboor & Hoang, Anh Tuan, 2023. "Poultry fat biodiesel as a fuel substitute in diesel-ethanol blends for DI-CI engine: Experimental, modeling and optimization," Energy, Elsevier, vol. 270(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kshirsagar, Charudatta M. & Anand, Ramanathan, 2017. "Artificial neural network applied forecast on a parametric study of Calophyllum inophyllum methyl ester-diesel engine out responses," Applied Energy, Elsevier, vol. 189(C), pages 555-567.
    2. Bahri, Bahram & Shahbakhti, Mahdi & Kannan, Kaushik & Aziz, Azhar Abdul, 2016. "Identification of ringing operation for low temperature combustion engines," Applied Energy, Elsevier, vol. 171(C), pages 142-152.
    3. Manieniyan, V. & Vinodhini, G. & Senthilkumar, R. & Sivaprakasam, S., 2016. "Wear element analysis using neural networks of a DI diesel engine using biodiesel with exhaust gas recirculation," Energy, Elsevier, vol. 114(C), pages 603-612.
    4. Mehra, Roopesh Kumar & Duan, Hao & Luo, Sijie & Rao, Anas & Ma, Fanhua, 2018. "Experimental and artificial neural network (ANN) study of hydrogen enriched compressed natural gas (HCNG) engine under various ignition timings and excess air ratios," Applied Energy, Elsevier, vol. 228(C), pages 736-754.
    5. Bendu, Harisankar & Deepak, B.B.V.L. & Murugan, S., 2017. "Multi-objective optimization of ethanol fuelled HCCI engine performance using hybrid GRNN–PSO," Applied Energy, Elsevier, vol. 187(C), pages 601-611.
    6. Yusri, I.M. & Abdul Majeed, A.P.P. & Mamat, R. & Ghazali, M.F. & Awad, Omar I. & Azmi, W.H., 2018. "A review on the application of response surface method and artificial neural network in engine performance and exhaust emissions characteristics in alternative fuel," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 665-686.
    7. Song Hu & Stefano d’Ambrosio & Roberto Finesso & Andrea Manelli & Mario Rocco Marzano & Antonio Mittica & Loris Ventura & Hechun Wang & Yinyan Wang, 2019. "Comparison of Physics-Based, Semi-Empirical and Neural Network-Based Models for Model-Based Combustion Control in a 3.0 L Diesel Engine," Energies, MDPI, vol. 12(18), pages 1-41, September.
    8. Subrata Bhowmik & Rajsekhar Panua & Subrata K Ghosh & Abhishek Paul & Durbadal Debroy, 2018. "Prediction of performance and exhaust emissions of diesel engine fuelled with adulterated diesel: An artificial neural network assisted fuzzy-based topology optimization," Energy & Environment, , vol. 29(8), pages 1413-1437, December.
    9. Roy, Sumit & Banerjee, Rahul & Bose, Probir Kumar, 2014. "Performance and exhaust emissions prediction of a CRDI assisted single cylinder diesel engine coupled with EGR using artificial neural network," Applied Energy, Elsevier, vol. 119(C), pages 330-340.
    10. Dhahad, Hayder Abed & Hasan, Ahmed Mudheher & Chaichan, Miqdam Tariq & Kazem, Hussein A., 2022. "Prognostic of diesel engine emissions and performance based on an intelligent technique for nanoparticle additives," Energy, Elsevier, vol. 238(PB).
    11. Çay, Yusuf & Korkmaz, Ibrahim & Çiçek, Adem & Kara, Fuat, 2013. "Prediction of engine performance and exhaust emissions for gasoline and methanol using artificial neural network," Energy, Elsevier, vol. 50(C), pages 177-186.
    12. Hosseini, Seyed Ehsan & Andwari, Amin Mahmoudzadeh & Wahid, Mazlan Abdul & Bagheri, Ghobad, 2013. "A review on green energy potentials in Iran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 533-545.
    13. Wong, Pak Kin & Wong, Ka In & Vong, Chi Man & Cheung, Chun Shun, 2015. "Modeling and optimization of biodiesel engine performance using kernel-based extreme learning machine and cuckoo search," Renewable Energy, Elsevier, vol. 74(C), pages 640-647.
    14. Yusaf, T.F. & Yousif, B.F. & Elawad, M.M., 2011. "Crude palm oil fuel for diesel-engines: Experimental and ANN simulation approaches," Energy, Elsevier, vol. 36(8), pages 4871-4878.
    15. Wu, Qibai & Xie, Xialin & Wang, Yaodong & Roskilly, Tony, 2018. "Effect of carbon coated aluminum nanoparticles as additive to biodiesel-diesel blends on performance and emission characteristics of diesel engine," Applied Energy, Elsevier, vol. 221(C), pages 597-604.
    16. Channapattana, S.V. & Pawar, Abhay A. & Kamble, Prashant G., 2017. "Optimisation of operating parameters of DI-CI engine fueled with second generation Bio-fuel and development of ANN based prediction model," Applied Energy, Elsevier, vol. 187(C), pages 84-95.
    17. Hosseinzadeh-Bandbafha, Homa & Kazemi Shariat Panahi, Hamed & Dehhaghi, Mona & Orooji, Yasin & Shahbeik, Hossein & Mahian, Omid & Karimi-Maleh, Hassan & Kalam, Md Abul & Salehi Jouzani, Gholamreza & M, 2023. "Applications of nanotechnology in biodiesel combustion and post-combustion stages," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    18. Ganesan, P. & Rajakarunakaran, S. & Thirugnanasambandam, M. & Devaraj, D., 2015. "Artificial neural network model to predict the diesel electric generator performance and exhaust emissions," Energy, Elsevier, vol. 83(C), pages 115-124.
    19. Bhowmik, Subrata & Paul, Abhishek & Panua, Rajsekhar & Ghosh, Subrata Kumar & Debroy, Durbadal, 2018. "Performance-exhaust emission prediction of diesosenol fueled diesel engine: An ANN coupled MORSM based optimization," Energy, Elsevier, vol. 153(C), pages 212-222.
    20. Liu, Jinlong & Huang, Qiao & Ulishney, Christopher & Dumitrescu, Cosmin E., 2021. "Machine learning assisted prediction of exhaust gas temperature of a heavy-duty natural gas spark ignition engine," Applied Energy, Elsevier, vol. 300(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:149:y:2020:i:c:p:951-961. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.